Text-to-speech (TTS) processing

ABSTRACT

A speech model is trained using multi-task learning. A first task may correspond to how well predicted audio matches training audio; a second task may correspond to a metric of perceived audio quality. The speech model may include, during training, layers related to the second task that are discarded at runtime.

BACKGROUND

Text-to-speech (TTS) systems convert written text to sound. This can beuseful to assist users of digital text media by synthesizing speechrepresenting text displayed on a computer screen. Speech recognitionsystems have also progressed to the point where humans can interact withand control computing devices by voice. TTS and speech recognitioncombined with natural language understanding processing techniquesenable speech-based user control and output of a computing device toperform tasks based on the user's spoken commands. The combination ofspeech recognition and natural language understanding processing isreferred to herein as speech processing. Such TTS and speech processingmay be used by computers, hand-held devices, telephone computer systems,kiosks, and a wide variety of other devices to improve human-computerinteractions.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description taken in conjunction with theaccompanying drawings.

FIG. 1 illustrates an exemplary system overview according to embodimentsof the present disclosure.

FIG. 2 illustrates components for performing text-to-speech (TTS)processing according to embodiments of the present disclosure.

FIGS. 3A and 3B illustrate speech synthesis using unit selectionaccording to embodiments of the present disclosure.

FIG. 4 illustrates speech synthesis using a Hidden Markov Model toperform TTS processing according to embodiments of the presentdisclosure.

FIG. 5 illustrates a speech model for generating audio data according toembodiments of the present disclosure.

FIGS. 6A and 6B illustrate sample models for generating audio samplecomponents according to embodiments of the present disclosure.

FIGS. 7A and 7B illustrate output models for generating audio samplesfrom audio sample components according to embodiments of the presentdisclosure.

FIGS. 8A and 8B illustrate conditioning models for upsampling audiometadata according to embodiments of the present disclosure.

FIG. 9 illustrates training a speech model according to embodiments ofthe present disclosure.

FIG. 10 illustrates runtime for a speech model according to embodimentsof the present disclosure.

FIG. 11 illustrates training a network using multi-task trainingaccording to embodiments of the present disclosure.

FIG. 12 illustrates training a network using a bottleneck according toembodiments of the present disclosure.

FIG. 13 illustrates is a block diagram conceptually illustrating examplecomponents of a remote device, such as server(s), that may be used withthe system according to embodiments of the present disclosure.

FIG. 14 illustrates a diagram conceptually illustrating distributedcomputing environment according to embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Text-to-speech (TTS) systems typically work using one of two techniques,each of which is described in more detail below. A first technique,called unit selection or concatenative TTS, processes and dividespre-recorded speech into many different segments of audio data, calledunits. The pre-recorded speech may be obtained by recording a humanspeaking many lines of text. Each segment that the speech is dividedinto may correspond to a particular audio unit such as a phoneme,diphone, or other length of sound. The individual units and datadescribing the units may be stored in a unit database, also called avoice corpus or voice inventory. When text data is received for TTSprocessing, the system may select the units that correspond to how thetext should sound and may combine them to generate, i.e., synthesize,the audio data that represents the desired speech.

A second technique, called parametric synthesis or statisticalparametric speech synthesis (SPSS), may use computer models and otherdata processing techniques to generate sound that is not based onpre-recorded speech (e.g., speech recorded prior to receipt of anincoming TTS request) but rather uses computing parameters to createoutput audio data. Vocoders are examples of components that can producespeech using parametric synthesis. Parametric synthesis may provide alarge range of diverse sounds that may be computer-generated at runtimefor a TTS request.

Each of these techniques, however, suffer from drawbacks. For unitselection, it may take many hours of recorded speech to create asufficient voice inventory for eventual unit selection. Further, inorder to have output speech having desired audio qualities, the humanspeaker used to record the speech needs to speak with the desired audioquality, which can be time consuming. For example, if the system is tobe configured to be able to synthesize whispered speech using unitselection, a human user may need to read text in a whisper for hours torecord enough sample speech to create a unit selection voice inventorythat can be used to synthesized whispered speech. The same is true forspeech with other qualities such as stern speech, excited speech, happyspeech, etc. Thus, a typical voice inventory only includes mostlyneutral speech or speech that does not typically include such extremeemotive or other non-standard audio characteristics. Further, aparticular voice inventory may be recorded by a particular voice actorfitting a certain voice profile and in a certain language, e.g., maleAustralian English, female Japanese, etc. To configure individual voiceinventories for all the combinations of language, voice profiles, audioqualities, etc., may be prohibitive.

Parametric synthesis, while typically more flexible at runtime, hashistorically not been able to create more natural sounding output speechthan unit selection. While a speech model may be trained to predict,based on input text, speech parameters—i.e., features that describe aspeech waveform to be created based on the speech parameters—parametricsystems still require that manually crafted assumptions be used tocreate the vocoders, which lead to a reduction in generated speechquality. Hybrid synthesis, which combines aspects of unit selection andparametric synthesis, may, however, still lead to less natural soundingoutput than custom-tailored unit selection due to reliance on parametricsynthesis when no appropriate unit may be suitable for given input text.

To address these deficiencies, one or more machine-learning model(s) maybe trained to directly generate audio data, for example audio outputwaveforms. The speech model may generate the audio datasample-by-sample. As explained in further detail below, the speech modelmay include a sample model, a conditioning model, and/or an outputmodel—which may also be referred to as a sample network, conditioningnetwork, and/or output network, respectively. The speech model maycreate tens of thousands of samples per second of audio; in someembodiments, the rate of output audio samples is 16 kHz. The speechmodel may be fully probabilistic and/or autoregressive; the predictivedistribution of each audio sample may be conditioned on all previousaudio samples. As explained in further detail below, the speech modelmay use causal convolutions to predict output audio; in someembodiments, the speech model uses dilated convolutions to generate anoutput sample using a greater area of input samples than would otherwisebe possible. The speech model may be trained using a conditioning modelthat conditions hidden layers of the network using linguistic contextfeatures, such as phoneme data. The audio output generated by the speechmodel may have higher audio quality than either unit selection orparametric synthesis.

The speech model may be trained to generate audio data corresponding toan audio output that resembles a vocal attribute—such as a style, tone,language, or other vocal attribute of a particular speaker—usingtraining data from one or more human speakers. Additional training, morevaried training data, a larger model, and/or other such factors may leadto an improvement in output audio quality, as measured by any of anumber of audio quality metrics. A point may be reached, however, atwhich additional training and/or model resources may not lead toadditional output audio quality or may lead to diminished returns inoutput audio quality. For example, the model may be trained simply bydetermining whether a waveform predicted by the speech model is“correct” or not based on, for example, how well it matches inputtraining data. This measure of “correctness” may not be, however,correlated with how a human being perceives the quality of the outputwaveform. In various embodiments of the present disclosure, the speechmodel may be trained using a primary or main task, such as one directlyrelated to output audio “correctness,” as well as one or more secondarytasks, which may relate to certain perceptible aspects of output audioquality. The secondary task(s) may be used to influence the training ofthe speech model at training time even though they are not directly usedat runtime, but may lead to improvements in output audio quality aboveand beyond those possible when training using only the main task.

An exemplary system overview is described in reference to FIG. 1. Asshown in FIG. 1, a system 100 may include one or more server(s) 120connected over a network 199 to one or more device(s) 110 that are localto a user 10. The server(s) 120 may be one physical machine capable ofperforming various operations described herein or may include severaldifferent machines, such as in a distributed computing environment, thatcombine to perform the operations described herein. The server(s) 120and/or device(s) 110 may produce output audio 15 in accordance with theembodiments described herein. A speech model is trained at least in partby training (130), using a first section of a model (which maycorrespond to a layer of the model), a hidden layer of the speech modelin accordance with a first task. The speech model is further trained(132) using a second section of the model, the hidden layer of thespeech model in accordance with a second task. The first section isincluded (134), and the second section is discarded (136). Text data isreceived (138) from a text-to-speech front end; text metadata is alsoreceived (140). First audio output is generated (142) using the textdata, the text metadata, and the speech model.

Components of a system that may be used to perform unit selection,parametric TTS processing, and/or model-based audio synthesis are shownin FIG. 2. In various embodiments of the present invention, model-basedsynthesis of audio data may be performed using by a speech model 222 anda TTS front-end 216. The TTS front-end 216 may be the same as front endsused in traditional unit selection or parametric systems. In otherembodiments, some or all of the components of the TTS front end 216 alsobased on other trained models. The present invention is not, however,limited to any particular type of TTS front end 216. The speech model222 may be included in a different component, such as a parametricengine component 232 or may be configured differently within the TTSmodule 295.

As shown in FIG. 2, the TTS component/processor 295 may include a TTSfront end 216, a speech synthesis engine 218, TTS unit storage 272, andTTS parametric storage 280. The TTS unit storage 272 may include, amongother things, voice inventories 278 a-288 n that may includepre-recorded audio segments (called units) to be used by the unitselection engine 230 when performing unit selection synthesis asdescribed below. The TTS parametric storage 280 may include, among otherthings, parametric settings 268 a-268 n that may be used by theparametric synthesis engine 232 when performing parametric synthesis asdescribed below. A particular set of parametric settings 268 maycorrespond to a particular voice profile (e.g., whispered speech,excited speech, etc.). The speech model 222 may be used to synthesizespeech without requiring the TTS unit storage 272 or the TTS parametricstorage 280, as described in greater detail below.

The TTS front end 216 transforms input text data 210 (for example fromsome speechlet component or other text source) into a symboliclinguistic representation, which may include linguistic contextfeatures, fundamental frequency information, or other such information,for processing by the speech synthesis engine 218. The input text data210 may be, for example, ASCII text, compressed text, or any othersimilar representation of text, and may be received from a user (from,e.g., a text-based query or command) or may be generated from audio data(from, e.g., an audio-based query or command). The TTS front end 216 mayalso process tags or other input data 215 input to the TTS component 295that indicate how specific words should be pronounced; the other inputdata 215 may, for example, indicate the desired output speech qualityusing tags formatted according to the speech synthesis markup language(SSML) or in some other form. For example, a first tag may be includedwith text marking the beginning of when text should be whispered (e.g.,<begin whisper>) and a second tag may be included with text marking theend of when text should be whispered (e.g., <end whisper>). The tags maybe included in the input text data and/or the text for a TTS request maybe accompanied by separate metadata indicating what text should bewhispered (or have some other indicated audio characteristic). Thespeech synthesis engine 218 compares the annotated phonetic units modelsand information stored in the TTS unit storage 272 and/or TTS parametricstorage 280 for converting the input text into speech. The TTS front end216 and speech synthesis engine 218 may include their owncontroller(s)/processor(s) and memory or they may use thecontroller/processor and memory of the server 120, device 110, or otherdevice, for example. Similarly, the instructions for operating the TTSfront end 216 and speech synthesis engine 218 may be located within theTTS component 295, within the memory and/or storage of the server 120,device 110, or within an external device.

Text data 210 input into a TTS component 295 may be sent to the TTSfront end 216 for processing. The front-end may include components forperforming text normalization, linguistic analysis, linguistic prosodygeneration, or other such components. During text normalization, the TTSfront end 216 may process the text input and generate standard text,converting such things as numbers, abbreviations (such as Apt., St.,etc.), symbols ($, %, etc.) into the equivalent of written out words.

During linguistic analysis the TTS front end 216 analyzes the languagein the normalized text to generate a sequence of phonetic unitscorresponding to the input text. This process may be referred to asgrapheme-to-phoneme conversion. Phonetic units include symbolicrepresentations of sound units to be eventually combined and output bythe system as speech. Various sound units may be used for dividing textfor purposes of speech synthesis. The TTS component 295 may processspeech based on phonemes (individual sounds), half-phonemes, di-phones(the last half of one phoneme coupled with the first half of theadjacent phoneme), bi-phones (two consecutive phonemes), syllables,words, phrases, sentences, or other units. Each word may be mapped toone or more phonetic units. Such mapping may be performed using alanguage dictionary stored by the system, for example in the TTS storagecomponent 272. The linguistic analysis performed by the TTS front end216 may also identify different grammatical components such as prefixes,suffixes, phrases, punctuation, syntactic boundaries, or the like. Suchgrammatical components may be used by the TTS component 295 to craft anatural sounding audio waveform output. The language dictionary may alsoinclude letter-to-sound rules and other tools that may be used topronounce previously unidentified words or letter combinations that maybe encountered by the TTS component 295. Generally, the more informationincluded in the language dictionary, the higher quality the speechoutput.

Based on the linguistic analysis the TTS front end 216 may then performlinguistic prosody generation where the phonetic units are annotatedwith desired prosodic characteristics, also called acoustic features,which indicate how the desired phonetic units are to be pronounced inthe eventual output speech. During this stage the TTS front end 216 mayconsider and incorporate any prosodic annotations (for example as inputtext metadata 215) that accompanied the text input to the TTS component295. Such acoustic features may include pitch, energy, duration, and thelike. Application of acoustic features may be based on prosodic modelsavailable to the TTS component 295. Such prosodic models indicate howspecific phonetic units are to be pronounced in certain circumstances. Aprosodic model may consider, for example, a phoneme's position in asyllable, a syllable's position in a word, a word's position in asentence or phrase, neighboring phonetic units, etc. As with thelanguage dictionary, prosodic model with more information may result inhigher quality speech output than prosodic models with less information.Further, a prosodic model and/or phonetic units may be used to indicateparticular speech qualities of the speech to be synthesized, where thosespeech qualities may match the speech qualities of input speech (forexample, the phonetic units may indicate prosodic characteristics tomake the ultimately synthesized speech sound like a whisper based on theinput speech being whispered).

The output of the TTS front end 216, which may be referred to as asymbolic linguistic representation, may include a sequence of phoneticunits annotated with prosodic characteristics. This symbolic linguisticrepresentation may be sent to the speech synthesis engine 218, which mayalso be known as a synthesizer, for conversion into an audio waveform ofspeech for output to an audio output device and eventually to a user.The speech synthesis engine 218 may be configured to convert the inputtext into high-quality natural-sounding speech in an efficient manner.Such high-quality speech may be configured to sound as much like a humanspeaker as possible, or may be configured to be understandable to alistener without attempts to mimic a precise human voice.

The speech synthesis engine 218 may perform speech synthesis using oneor more different methods. In one method of synthesis called unitselection, described further below, a unit selection engine 230 matchesthe symbolic linguistic representation created by the TTS front end 216against a database of recorded speech, such as a database (e.g., TTSunit storage 272) storing information regarding one or more voicecorpuses (e.g., voice inventories 278 a-n). Each voice inventory maycorrespond to various segments of audio that was recorded by a speakinghuman, such as a voice actor, where the segments are stored in anindividual inventory 278 as acoustic units (e.g., phonemes, diphones,etc.). Each stored unit of audio may also be associated with an indexlisting various acoustic properties or other descriptive informationabout the unit. Each unit includes an audio waveform corresponding witha phonetic unit, such as a short .wav file of the specific sound, alongwith a description of various features associated with the audiowaveform. For example, an index entry for a particular unit may includeinformation such as a particular unit's pitch, energy, duration,harmonics, center frequency, where the phonetic unit appears in a word,sentence, or phrase, the neighboring phonetic units, or the like. Theunit selection engine 230 may then use the information about each unitto select units to be joined together to form the speech output.

The unit selection engine 230 matches the symbolic linguisticrepresentation against information about the spoken audio units in thedatabase. The unit database may include multiple examples of phoneticunits to provide the system with many different options forconcatenating units into speech. Matching units which are determined tohave the desired acoustic qualities to create the desired output audioare selected and concatenated together (for example by a synthesiscomponent 220) to form output audio data 290 representing synthesizedspeech. The output audio data 290 may be formatted as MP3, OGG, WAV, orother audio data formats, and may have a data rate of 16 kHz. The TTSmodule 295 may further output other output data 285, which may includeaudio data such as tones or beeps, similarly formatted as MP3, OGG, WAV,or other audio data formats, text data, or any other data format. Usingall the information in the unit database, a unit selection engine 230may match units to the input text to select units that can form anatural sounding waveform. One benefit of unit selection is that,depending on the size of the database, a natural sounding speech outputmay be generated. As described above, the larger the unit database ofthe voice corpus, the more likely the system will be able to constructnatural sounding speech.

In another method of synthesis called parametric synthesis parameterssuch as frequency, volume, noise, are varied by a parametric synthesisengine 232, digital signal processor or other audio generation device tocreate an artificial speech waveform output. Parametric synthesis uses acomputerized voice generator, sometimes called a vocoder. Parametricsynthesis may use an acoustic model and various statistical techniquesto match a symbolic linguistic representation with desired output speechparameters. Using parametric synthesis, a computing system (for example,a synthesis component 220) can generate audio waveforms having thedesired acoustic properties. Parametric synthesis may include theability to be accurate at high processing speeds, as well as the abilityto process speech without large databases associated with unitselection, but also may produce an output speech quality that may notmatch that of unit selection. Unit selection and parametric techniquesmay be performed individually or combined together and/or combined withother synthesis techniques to produce speech audio output.

The TTS component 295 may be configured to perform TTS processing inmultiple languages. For each language, the TTS component 295 may includespecially configured data, instructions and/or components to synthesizespeech in the desired language(s). To improve performance, the TTScomponent 295 may revise/update the contents of the TTS storage 280based on feedback of the results of TTS processing, thus enabling theTTS component 295 to improve speech recognition.

The TTS storage module 295 may be customized for an individual userbased on his/her individualized desired speech output. In particular,the speech unit stored in a unit database may be taken from input audiodata of the user speaking. For example, to create the customized speechoutput of the system, the system may be configured with multiple voiceinventories 278 a-278 n, where each unit database is configured with adifferent “voice” to match desired speech qualities. Such voiceinventories may also be linked to user accounts. The voice selected bythe TTS component 295 to synthesize the speech. For example, one voicecorpus may be stored to be used to synthesize whispered speech (orspeech approximating whispered speech), another may be stored to be usedto synthesize excited speech (or speech approximating excited speech),and so on. To create the different voice corpuses a multitude of TTStraining utterances may be spoken by an individual (such as a voiceactor) and recorded by the system. The audio associated with the TTStraining utterances may then be split into small audio segments andstored as part of a voice corpus. The individual speaking the TTStraining utterances may speak in different voice qualities to create thecustomized voice corpuses, for example the individual may whisper thetraining utterances, say them in an excited voice, and so on. Thus theaudio of each customized voice corpus may match the respective desiredspeech quality. The customized voice inventory 278 may then be usedduring runtime to perform unit selection to synthesize speech having aspeech quality corresponding to the input speech quality.

Additionally, parametric synthesis may be used to synthesize speech withthe desired speech quality. For parametric synthesis, parametricfeatures may be configured that match the desired speech quality. Ifsimulated excited speech was desired, parametric features may indicatean increased speech rate and/or pitch for the resulting speech. Manyother examples are possible. The desired parametric features forparticular speech qualities may be stored in a “voice” profile (e.g.,parametric settings 268) and used for speech synthesis when the specificspeech quality is desired. Customized voices may be created based onmultiple desired speech qualities combined (for either unit selection orparametric synthesis). For example, one voice may be “shouted” whileanother voice may be “shouted and emphasized.” Many such combinationsare possible.

Unit selection speech synthesis may be performed as follows. Unitselection includes a two-step process. First a unit selection engine 230determines what speech units to use and then it combines them so thatthe particular combined units match the desired phonemes and acousticfeatures and create the desired speech output. Units may be selectedbased on a cost function which represents how well particular units fitthe speech segments to be synthesized. The cost function may represent acombination of different costs representing different aspects of howwell a particular speech unit may work for a particular speech segment.For example, a target cost indicates how well an individual given speechunit matches the features of a desired speech output (e.g., pitch,prosody, etc.). A join cost represents how well a particular speech unitmatches an adjacent speech unit (e.g., a speech unit appearing directlybefore or directly after the particular speech unit) for purposes ofconcatenating the speech units together in the eventual synthesizedspeech. The overall cost function is a combination of target cost, joincost, and other costs that may be determined by the unit selectionengine 230. As part of unit selection, the unit selection engine 230chooses the speech unit with the lowest overall combined cost. Forexample, a speech unit with a very low target cost may not necessarilybe selected if its join cost is high.

The system may be configured with one or more voice corpuses for unitselection. Each voice corpus may include a speech unit database. Thespeech unit database may be stored in TTS unit storage 272 or in anotherstorage component. For example, different unit selection databases maybe stored in TTS unit storage 272. Each speech unit database (e.g.,voice inventory) includes recorded speech utterances with theutterances' corresponding text aligned to the utterances. A speech unitdatabase may include many hours of recorded speech (in the form of audiowaveforms, feature vectors, or other formats), which may occupy asignificant amount of storage. The unit samples in the speech unitdatabase may be classified in a variety of ways including by phoneticunit (phoneme, diphone, word, etc.), linguistic prosodic label, acousticfeature sequence, speaker identity, etc. The sample utterances may beused to create mathematical models corresponding to desired audio outputfor particular speech units. When matching a symbolic linguisticrepresentation the speech synthesis engine 218 may attempt to select aunit in the speech unit database that most closely matches the inputtext (including both phonetic units and prosodic annotations). Generallythe larger the voice corpus/speech unit database the better the speechsynthesis may be achieved by virtue of the greater number of unitsamples that may be selected to form the precise desired speech output.An example of how unit selection is performed is illustrated in FIGS. 3Aand 3B.

For example, as shown in FIG. 3A, a target sequence of phonetic units310 to synthesize the word “hello” is determined by a TTS device. Asillustrated, the phonetic units 310 are individual diphones, thoughother units, such as phonemes, etc. may be used. A number of candidateunits may be stored in the voice corpus. For each phonetic unitindicated as a match for the text, there are a number of potentialcandidate units (represented by columns 306, 308, 310, 312 and 314)available. Each candidate unit represents a particular recording of thephonetic unit with a particular associated set of acoustic andlinguistic features. For example, column 306 represents potentialdiphone units that correspond to the sound of going from silence (#) tothe middle of an H sound, column 306 represents potential diphone unitsthat correspond to the sound of going from the middle of an H sound tothe middle of an E (in hello) sound, column 310 represents potentialdiphone units that correspond to the sound of going from the middle ofan E (in hello) sound to the middle of an L sound, column 312 representspotential diphone units that correspond to the sound of going from themiddle of an L sound to the middle of an O (in hello sound), and column314 represents potential diphone units that correspond to the sound ofgoing from the middle of an O (in hello sound) to silence.

The individual potential units are selected based on the informationavailable in the voice inventory about the acoustic properties of thepotential units and how closely each potential unit matches the desiredsound for the target unit sequence 302. How closely each respective unitmatches the desired sound will be represented by a target cost. Thus,for example, unit #-H₁ will have a first target cost, unit #-H₂ willhave a second target cost, unit #-H₃ will have a third target cost, andso on.

The TTS system then creates a graph of potential sequences of candidateunits to synthesize the available speech. The size of this graph may bevariable based on certain device settings. An example of this graph isshown in FIG. 3B. A number of potential paths through the graph areillustrated by the different dotted lines connecting the candidateunits. A Viterbi algorithm may be used to determine potential pathsthrough the graph. Each path may be given a score incorporating both howwell the candidate units match the target units (with a high scorerepresenting a low target cost of the candidate units) and how well thecandidate units concatenate together in an eventual synthesized sequence(with a high score representing a low join cost of those respectivecandidate units). The TTS system may select the sequence that has thelowest overall cost (represented by a combination of target costs andjoin costs) or may choose a sequence based on customized functions fortarget cost, join cost or other factors. For illustration purposes, thetarget cost may be thought of as the cost to select a particular unit inone of the columns of FIG. 3B whereas the join cost may be thought of asthe score associated with a particular path from one unit in one columnto another unit of another column. The candidate units along theselected path through the graph may then be combined together to form anoutput audio waveform representing the speech of the input text. Forexample, in FIG. 3B the selected path is represented by the solid line.Thus units #-H₂, H-E₁, E-L₄, L-O₃, and O-#₄ may be selected, and theirrespective audio concatenated by synthesis component 220, to synthesizeaudio for the word “hello.” This may continue for the input text data210 to determine output audio data.

Vocoder-based parametric speech synthesis may be performed as follows. ATTS component 295 may include an acoustic model, or other models, whichmay convert a symbolic linguistic representation into a syntheticacoustic waveform of the text input based on audio signal manipulation.The acoustic model includes rules which may be used by the parametricsynthesis engine 232 to assign specific audio waveform parameters toinput phonetic units and/or prosodic annotations. The rules may be usedto calculate a score representing a likelihood that a particular audiooutput parameter(s) (such as frequency, volume, etc.) corresponds to theportion of the input symbolic linguistic representation from the TTSfront end 216.

The parametric synthesis engine 232 may use a number of techniques tomatch speech to be synthesized with input phonetic units and/or prosodicannotations. One common technique is using Hidden Markov Models (HMMs).HMMs may be used to determine probabilities that audio output shouldmatch textual input. HMMs may be used to translate from parameters fromthe linguistic and acoustic space to the parameters to be used by avocoder (the digital voice encoder) to artificially synthesize thedesired speech. Using HMMs, a number of states are presented, in whichthe states together represent one or more potential acoustic parametersto be output to the vocoder and each state is associated with a model,such as a Gaussian mixture model. Transitions between states may alsohave an associated probability, representing a likelihood that a currentstate may be reached from a previous state. Sounds to be output may berepresented as paths between states of the HMM and multiple paths mayrepresent multiple possible audio matches for the same input text. Eachportion of text may be represented by multiple potential statescorresponding to different known pronunciations of phonemes and theirparts (such as the phoneme identity, stress, accent, position, etc.). Aninitial determination of a probability of a potential phoneme may beassociated with one state. As new text is processed by the speechsynthesis engine 218, the state may change or stay the same, based onthe processing of the new text. For example, the pronunciation of apreviously processed word might change based on later processed words. AViterbi algorithm may be used to find the most likely sequence of statesbased on the processed text. The HMMs may generate speech inparameterized form including parameters such as fundamental frequency(f0), noise envelope, spectral envelope, etc. that are translated by avocoder into audio segments. The output parameters may be configured forparticular vocoders such as a STRAIGHT vocoder, TANDEM-STRAIGHT vocoder,WORLD vocoder, HNM (harmonic plus noise) based vocoders, CELP(code-excited linear prediction) vocoders, GlottHMM vocoders, HSM(harmonic/stochastic model) vocoders, or others.

An example of HMM processing for speech synthesis is shown in FIG. 4. Asample input phonetic unit may be processed by a parametric synthesisengine 232. The parametric synthesis engine 232 may initially assign aprobability that the proper audio output associated with that phoneme isrepresented by state S₀ in the Hidden Markov Model illustrated in FIG.4. After further processing, the speech synthesis engine 218 determineswhether the state should either remain the same, or change to a newstate. For example, whether the state should remain the same 404 maydepend on the corresponding transition probability (written as P(S₀|S₀),meaning the probability of going from state S₀ to S₀) and how well thesubsequent frame matches states S₀ and S₁. If state S₁ is the mostprobable, the calculations move to state S₁ and continue from there. Forsubsequent phonetic units, the speech synthesis engine 218 similarlydetermines whether the state should remain at S₁, using the transitionprobability represented by P(S₁|S₁) 408, or move to the next state,using the transition probability P(S₂|S₁) 410. As the processingcontinues, the parametric synthesis engine 232 continues calculatingsuch probabilities including the probability 412 of remaining in stateS₂ or the probability of moving from a state of illustrated phoneme/E/to a state of another phoneme. After processing the phonetic units andacoustic features for state S₂, the speech recognition may move to thenext phonetic unit in the input text.

The probabilities and states may be calculated using a number oftechniques. For example, probabilities for each state may be calculatedusing a Gaussian model, Gaussian mixture model, or other technique basedon the feature vectors and the contents of the TTS storage 280.Techniques such as maximum likelihood estimation (MLE) may be used toestimate the probability of particular states.

In addition to calculating potential states for one audio waveform as apotential match to a phonetic unit, the parametric synthesis engine 232may also calculate potential states for other potential audio outputs(such as various ways of pronouncing a particular phoneme or diphone) aspotential acoustic matches for the acoustic unit. In this mannermultiple states and state transition probabilities may be calculated.

The probable states and probable state transitions calculated by theparametric synthesis engine 232 may lead to a number of potential audiooutput sequences. Based on the acoustic model and other potentialmodels, the potential audio output sequences may be scored according toa confidence level of the parametric synthesis engine 232. The highestscoring audio output sequence, including a stream of parameters to besynthesized, may be chosen and digital signal processing may beperformed by a vocoder or similar component to create an audio outputincluding synthesized speech waveforms corresponding to the parametersof the highest scoring audio output sequence and, if the proper sequencewas selected, also corresponding to the input text. The differentparametric settings 268, which may represent acoustic settings matchinga particular parametric “voice”, may be used by the synthesis component220 to ultimately create the output audio data 290.

FIG. 5 illustrates an embodiment of the speech model 222, which mayinclude a sample model 502, an output model 504, and a conditioningmodel 506, each of which are described in greater detail below. The TTSfront end 216 may receive input text data 210 and generate metadata 508,which may be formatted as text, as a feature vector, or as any otherformat, and may include input text, phoneme data, duration data, and/orfundamental frequency (F0) data, as described in greater detail below.During training, the metadata 508 may include prerecorded audio data andcorresponding text data created for training the speech model 222. Insome embodiments, during runtime, the TTS front end 216 includes afirst-pass speech synthesis engine that creates speech using, forexample, the unit selection and/or parametric synthesis techniquesdescribed above.

The sample model 502 may include a dilated convolution component 512.The dilated convolution component 512 performs a filter over an area ofthe input larger than the length of the filter by skipping input valueswith a certain step size, depending on the layer of the convolution. Forexample, the dilated convolution component 512 may operate on everysample in the first layer, every second sample in the second layer,every fourth sample in the third layer, and so on. The dilatedconvolution component 512 may effectively allow the speech model 222 tooperate on a coarser scale than with a normal convolution. The input tothe dilated convolution component 512 may be, for example, a vector ofsize r created by performing a 2×1 convolution and a tanh function (alsoknown as a hyperbolic tangent function) on an input audio one-hotvector. The output of the dilated convolution component 512 may be avector of size 2r.

An activation/combination component 514 may combine the output of thedilated convolution component 512 with one or more outputs of theconditioning model 506, as described in greater detail below, and/oroperated on by one or more activation functions, such as tanh or sigmoidfunctions, as also described in greater detail below. Theactivation/combination component 514 may combine the 2 r vector outputby the dilated convolution component 512 into a vector of size r. Thepresent disclosure is not, however, limited to any particulararchitecture related to activation and/or combination.

The output of the activation/combination component 514 may be combined,using a combination component 516, with the input to the dilatedconvolution component 512. In some embodiments, prior to thiscombination, the output of the activation/combination component 514 isconvolved by a second convolution component 518, which may be a 1×1convolution on r values.

The sample model 502 may include one or more layers, each of which mayinclude some or all of the components described above. In someembodiments, the sample model 502 includes 40 layers, which may beconfigured in four blocks with ten layers per block; the output of eachcombination component 516, which may be referred to as residualchannels, may include 128 values; and the output of eachconvolution/affine component 520, which may be referred to as skipchannels or skip outputs, may include 1024 values. The dilationperformed by the dilated convolution component 512 may be 2^(n) for eachlayer n, and may be reset at each block.

The first layer may receive the metadata 508 as input; the output of thefirst layer, corresponding to the output of the combination component514, may be received by the dilated convolution component 512 of thesecond layer. The output of the last layer may be unused. As one ofskill in the art will understand, a greater number of layers may resultin higher-quality output speech at the cost of greater computationalcomplexity and/or cost; any number of layers is, however, within thescope of the present disclosure. In some embodiments, the number oflayers may be limited in the latency between the first layer and thelast layer, as determined by the characteristics of a particularcomputing system, and the output audio rate (e.g., 16 kHz).

A convolution/affine component 520 may receive the output (of size r) ofthe activation/combination component 514 and perform a convolution(which may be a 1×1 convolution) or an affine transformation to producean output of size s, wherein s<r. In some embodiments, this operationmay also be referred to as a skip operation or a skip-connectionoperation, in which only a subset of the outputs from the layers of thesample model 502 are used as input by the convolution/affine component520. The output of the convolution/affine component 520 may be combinedusing a second combination component 522, the output of which may bereceived by an output model 524 to create output audio data 290, whichis also explained in greater detail below. One or more outputs of theoutput model 524 may be fed back to the sample model 502 instead of orin addition to the metadata 508.

FIGS. 6A and 6B illustrate embodiments of the sample model 502.Referring first to FIG. 6A, a 2×1 dilated convolution component 602receives a vector of size r from the TTS front end 216 or from aprevious layer of the sample model 502 and produces an output of size2r. A split component 604 splits this output into two vectors, each ofsize r; these vectors are combined, using combination components 606 and608, which the output of the conditioning model 506, which has beensimilarly split by a second split component 610. A tanh component 612performs a tanh function on the first combination, a sigmoid (σ)component 614 performs a sigmoid function on the second combination, andthe results of each function are combined using a third combinationcomponent 616. An affine transformation component 618 performs an affinetransformation on the result and outputs the result to the output model524. A fourth combination component 620 combines the output of theprevious combination with the input and outputs the result to the nextlayer, if any.

Referring to FIG. 6B, many of the same functions described above withreference to FIG. 6A are performed. In this embodiment, however, a 1×1convolution component 622 performs a 1×1 convolution on the output ofthe third combination component 616 in lieu of the affine transformationperformed by the affine transformation component 618 of FIG. 6A. Inaddition, a second 1×1 convolution component 624 performs a second 1×1convolution on the output of the third combination component 616, theoutput of which is received by the fourth combination component 620.

FIGS. 7A and 7B illustrate embodiments of the output model 524.Referring first to FIG. 7A, a first rectified linear unit (ReLU) 702 mayperform a first rectification function on the output of the sample model502, and a first affine transform component 704 may perform a firstaffine transform on the output of the ReLU 702. The input vector to thefirst affine transform component 704 may be of size s, and the outputmay be of size a. In various embodiments, s>a; a may represent thenumber of frequency bins corresponding to the output audio and may be ofsize ten. A second ReLU component 706 performs a second rectificationfunction, and a second affine transform component 708 performs a secondaffine transform. A softmax component 710 may be used to generate outputaudio data 290 from the output of the second affine transform component708 by, for example, transforming its input into a number of valuesbetween 0 and 1 in which all of the values add up to 1. FIG. 7B issimilar to FIG. 7A but replaces the affine transformation components704, 708 with 1×1 convolution components 712, 714.

FIGS. 8A and 8B illustrate embodiments of the conditioning model 216. Invarious embodiments, the metadata 508 received by the conditioning model216 is represented by a lower sample rate than the text/audio datareceived by the sample model 502. In some embodiments, the sample model502 receives data sampled at 16 kHz while the conditioning modelreceives data sampled at 256 Hz. The conditioning model 216 may thusupsample the lower-rate input so that it matches the higher-rate inputreceived by the sample model 502.

Referring to FIG. 8A, the text and/or audio metadata 508 is received bya first forward long short-term memory (LSTM) 802 and a first backwardLSTM 804. The input metadata 508 may include linguistic contextfeatures, fundamental frequency data, grapheme-to-phoneme data, durationprediction data, or any other type of data. In some embodiments, theinput metadata 508 includes 86 linguistic context features; any numberof context features is, however, within the scope of the presentdisclosure. The outputs of both LSTMs 802, 804 may be received by afirst stack element 818, which may combine the outputs 802, 804 bysummation, by concatenation, or by any other combination. The output ofthe first stack element 818 is received by both a second forward LSTM806 and a second backward LSTM 808. The outputs of the second LSTMs 806,808 are combined using a second stack element 824, the output of whichis received by an affine transform component 810 and upsampled by anupsampling component 812. The output of the upsampling component 812, asmentioned above, is combined with the sample model 502 using anactivation/combination element 514. This output of the upsamplingcomponent 812 represents an upsampled version of the metadata 508, maybe referred to herein as conditioning data or prosody data, and mayinclude numbers or vectors of numbers.

With reference to FIG. 8B, in this embodiment, the input text metadata215 is received by a first forward quasi-recurrent neural network (QRNN)814 and first backward QRNN 816, the outputs of which are combined by afirst stack component 818. The output of the stack component 818 isreceived by a second forward QRNN 820 and a second backward QRNN 822.The outputs of the second QRNNs 820, 822 are combined by a second stackcomponent 824, interleaved by an interleave component 826, and thenupsampled by the upsampling component 812.

As mentioned above, the speech model 222 may be used with existing TTSfront ends, such as those developed for use with the unit selection andparametric speech systems described above. In other embodiments,however, the TTS front end may include one or more additional modelsthat may be trained using training data, similar to how the speech model222 may be trained.

FIG. 9 illustrates an embodiment of such a model-based TTS front end216. FIG. 9 illustrates the training of the TTS front end XB16 and ofthe speech model 222; FIG. SSK, described in more detail below,illustrates the trained TTS front end XB16 and speech model 222 atruntime. Training audio 902, which may be formatted as, for example,MP3, OGG, or WAV formats, and corresponding training text 904, which maybe ASCII or similar format text, may be used to train the models. Thetraining audio 902 may be captured using a human voice, and the trainingtext 904 may be generated using a speech-to-text system and/or by ahuman transcriber.

A grapheme-to-phoneme model 906 may be trained to convert the trainingtext 904 from text (e.g., text characters) to phonemes, which may beencoded using a phonemic alphabet such as ARPABET. Thegrapheme-to-phoneme model 906 may reference a phoneme dictionary 908. Asegmentation model 910 may be trained to locate phoneme boundaries inthe voice dataset using an output of the grapheme-to-phoneme model 906and the training audio 902. Given this input, the segmentation model 910may be trained to identify where in the training audio 902 each phonemebegins and ends. An acoustic feature model 912 may be trained to predictacoustic features of the training audio, such as whether a phoneme isvoiced, the fundamental frequency (F0) throughout the phoneme'sduration, or other such features. A phoneme duration prediction model916 may be trained to predict the temporal duration of phonemes in aphoneme sequence (e.g., an utterance). The speech model receives, asinputs, the outputs of the grapheme-to-phoneme model 906, the durationprediction model 916, and the acoustic features model 912 and may betrained to synthesize audio at a high sampling rate, as described above.

FIG. 10 illustrates use of the model-based TTS front end 216 and speechmodel 222 during runtime. The grapheme-to-phoneme model 906 receivesinput text data 210 and locates phoneme boundaries therein. Using thisdata, the acoustic features model 912 predicts acoustic features, suchas fundamental frequencies of phonemes, and the duration predictionmodel 916 predicts durations of phonemes. Using the phoneme data,acoustic data, and duration, data, the speech model 222 synthesizesoutput audio data 290.

FIG. 11 illustrates training a network, such as the sample model 502,conditioning subnetwork 506, and/or output model 524, using input data1102, a main task 1104, and at least one secondary task 1106. This typeof learning may be referred to as multi-task learning. As mentionedabove, the network may include an input layer 1108, an output layer1112, and one or more hidden layers 1110. As also mentioned above, themain task 1104 may be training the network to match the output audiodata 290 generated by the network as closely as possible to the trainingdata 902; this matching may, however, lead to changes in the networkduring training that do not improve the perceived quality of the outputaudio data 290 to a human listener. As a result, the training networkmay include complexity that has no benefit to a human listener.

In some embodiments, the one or more secondary tasks 1106 are addedduring training to improve the quality of the output audio data 290,reduce the size of the speech model 222, or both. The secondary task1106 may be based on, for example, one or more standards of theperceptual evaluation of speech quality (PESQ). The secondary task 1106may also or instead be based on, for example, the short-term powerspectrum of the training data 902 and/or output audio data 290represented by, for example, the mel-frequency cepstrum (MFC) of thetraining data 902, and/or output audio data 290. Basing the secondarytask 1106 on the MFC may, for example, alter the frequency spectrum ofthe output audio data 290 to emphasize frequencies favored by the humanear and de-emphasize frequencies disfavored by the human ear. Any typeof secondary task 1106 that improves the perceived quality of the outputaudio data 290 is, however, within the scope of the present disclosure.

In some embodiments, additional sections of the model—such as layers,nodes, components, or other elements—may be added to the speech model222 to better accommodate the training of the secondary task 1106. Theseadditional sections or layers may be added to, for example, the softmaxcomponent 710 in the output model 524, to the LSTM components 802, 804,806, 808 in the conditioning model 506, and/or to other parts of thespeech model 222. Some layers, such as the additional softmax layers,may correspond to the second task; other layers also used for training,such as the additional LSTM layers, may correspond to a third task alsorelated to perceived quality, as recited above. In some embodiments,these additional sections or layers modify the training of the samplemodel 502 during training but are discarded and/or inactive in thespeech model 222 during runtime.

In some embodiments, with reference to FIG. 12, a bottleneck feature1202 may be added to one or more hidden layers 1110. The bottleneckfeature 1202 corresponds to removing one or more nodes from the hiddenlayer 1110 during training; the reduced number of nodes of the hiddenlayer 1110 may result in a greater control in training the hidden layer1110 and/or a faster training time. In some embodiments, the values ofthe nodes in the bottleneck feature 1202 of the hidden layer 1110 areused as inputs to train a second model that does not include thebottleneck feature 1202; this second model may thereafter be used atruntime.

Audio waveforms (such as output audio data 290) including the speechoutput from the TTS component 295 may be sent to an audio outputcomponent, such as a speaker for playback to a user or may be sent fortransmission to another device, such as another server 120, for furtherprocessing or output to a user. Audio waveforms including the speech maybe sent in a number of different formats such as a series of featurevectors, uncompressed audio data, or compressed audio data. For example,audio speech output may be encoded and/or compressed by anencoder/decoder (not shown) prior to transmission. The encoder/decodermay be customized for encoding and decoding speech data, such asdigitized audio data, feature vectors, etc. The encoder/decoder may alsoencode non-TTS data of the system, for example using a general encodingscheme such as .zip, etc.

Although the above discusses a system, one or more components of thesystem may reside on any number of devices. FIG. 13 is a block diagramconceptually illustrating example components of a remote device, such asserver(s) 120, which may determine which portion of a textual work toperform TTS processing on and perform TTS processing to provide an audiooutput. Multiple such servers 120 may be included in the system, such asone server 120 for determining the portion of the textual to processusing TTS processing, one server 120 for performing TTS processing, etc.In operation, each of these devices may include computer-readable andcomputer-executable instructions that reside on the server(s) 120, aswill be discussed further below. The term “server” as used herein mayrefer to a traditional server as understood in a server/client computingstructure but may also refer to a number of different computingcomponents that may assist with the operations discussed herein. Forexample, a server may include one or more physical computing components(such as a rack server) that are connected to other devices/componentseither physically and/or over a network and is capable of performingcomputing operations. A server may also include one or more virtualmachines that emulates a computer system and is run on one or acrossmultiple devices. A server may also include other combinations ofhardware, software, firmware, or the like to perform operationsdiscussed herein. The server(s) may be configured to operate using oneor more of a client-server model, a computer bureau model, gridcomputing techniques, fog computing techniques, mainframe techniques,utility computing techniques, a peer-to-peer model, sandbox techniques,or other computing techniques.

Each server 120 may include one or more controllers/processors (1302),which may each include a central processing unit (CPU) for processingdata and computer-readable instructions, and a memory (1304) for storingdata and instructions of the respective device. The memories (1304) mayindividually include volatile random access memory (RAM), non-volatileread only memory (ROM), non-volatile magnetoresistive (MRAM) and/orother types of memory. Each server may also include a data storagecomponent (1306), for storing data and controller/processor-executableinstructions. Each data storage component may individually include oneor more non-volatile storage types such as magnetic storage, opticalstorage, solid-state storage, etc. Each device may also be connected toremovable or external non-volatile memory and/or storage (such as aremovable memory card, memory key drive, networked storage, etc.)through respective input/output device interfaces (1308). The storagecomponent 1306 may include storage for various data including ASRmodels, NLU knowledge base, entity library, speech quality models, TTSvoice unit storage, and other storage used to operate the system.

Computer instructions for operating each server (120) and its variouscomponents may be executed by the respective server'scontroller(s)/processor(s) (1302), using the memory (1304) as temporary“working” storage at runtime. A server's computer instructions may bestored in a non-transitory manner in non-volatile memory (1304), storage(1306), or an external device(s). Alternatively, some or all of theexecutable instructions may be embedded in hardware or firmware on therespective device in addition to or instead of software.

The server (120) may include input/output device interfaces (1308). Avariety of components may be connected through the input/output deviceinterfaces, as will be discussed further below. Additionally, the server(120) may include an address/data bus (1310) for conveying data amongcomponents of the respective device. Each component within a server(120) may also be directly connected to other components in addition to(or instead of) being connected to other components across the bus(1310).

One or more servers 120 may include the TTS component 295, or othercomponents capable of performing the functions described above.

As described above, the storage component 1306 may include storage forvarious data including speech quality models, TTS voice unit storage,and other storage used to operate the system and perform the algorithmsand methods described above. The storage component 1306 may also storeinformation corresponding to a user profile, including purchases of theuser, returns of the user, recent content accessed, etc.

As noted above, multiple devices may be employed in a single system. Insuch a multi-device system, each of the devices may include differentcomponents for performing different aspects of the system. The multipledevices may include overlapping components. The components of thedevices 110 and server(s) 120, as described with reference to FIG. 13,are exemplary, and may be located a stand-alone device or may beincluded, in whole or in part, as a component of a larger device orsystem.

As illustrated in FIG. 14, multiple devices may contain components ofthe system and the devices may be connected over a network 199. Thenetwork 199 is representative of any type of communication network,including data and/or voice network, and may be implemented using wiredinfrastructure (e.g., cable, CATS, fiber optic cable, etc.), a wirelessinfrastructure (e.g., WiFi, RF, cellular, microwave, satellite,Bluetooth, etc.), and/or other connection technologies. Devices may thusbe connected to the network 199 through either wired or wirelessconnections. Network 199 may include a local or private network or mayinclude a wide network such as the internet. For example, server(s) 120,smart phone 110 b, networked microphone(s) 1404, networked audio outputspeaker(s) 1406, tablet computer 110 d, desktop computer 110 e, laptopcomputer 110 f, speech device 110 a, refrigerator 110 c, etc. may beconnected to the network 199 through a wireless service provider, over aWiFi or cellular network connection or the like.

As described above, a device, may be associated with a user profile. Forexample, the device may be associated with a user identification (ID)number or other profile information linking the device to a useraccount. The user account/ID/profile may be used by the system toperform speech controlled commands (for example commands discussedabove). The user account/ID/profile may be associated with particularmodel(s) or other information used to identify received audio, classifyreceived audio (for example as a specific sound described above),determine user intent, determine user purchase history, content accessedby or relevant to the user, etc.

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems, speech processing systems, anddistributed computing environments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers and speech processing should recognizethat components and process steps described herein may beinterchangeable with other components or steps, or combinations ofcomponents or steps, and still achieve the benefits and advantages ofthe present disclosure. Moreover, it should be apparent to one skilledin the art, that the disclosure may be practiced without some or all ofthe specific details and steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storage mediamay be implemented by a volatile computer memory, non-volatile computermemory, hard drive, solid-state memory, flash drive, removable diskand/or other media. In addition, components of one or more of thecomponents, components and engines may be implemented as in firmware orhardware, including digital filters (e.g., filters configured asfirmware to a digital signal processor (DSP)).

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems, speech processing systems, anddistributed computing environments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers and speech processing should recognizethat components and process steps described herein may beinterchangeable with other components or steps, or combinations ofcomponents or steps, and still achieve the benefits and advantages ofthe present disclosure. Moreover, it should be apparent to one skilledin the art, that the disclosure may be practiced without some or all ofthe specific details and steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storage mediamay be implemented by a volatile computer memory, non-volatile computermemory, hard drive, solid-state memory, flash drive, removable diskand/or other media. In addition, components of one or more of thecomponents and engines may be implemented as in firmware or hardware,such as the acoustic front end 256, which comprise among other things,analog and/or digital filters (e.g., filters configured as firmware to adigital signal processor (DSP)).

As used in this disclosure, the term “a” or “one” may include one ormore items unless specifically stated otherwise. Further, the phrase“based on” is intended to mean “based at least in part on” unlessspecifically stated otherwise.

What is claimed is:
 1. A computer-implemented method for generatingspeech from text, the method comprising: training, using multi-tasklearning, to create a speech model, wherein: the speech model includes:a sample model configured to input text data and output audio samples, aconditioning model configured to input text metadata corresponding tothe input text data and to condition the sample model, and an outputmodel configured to input the output audio samples and output audiooutput data, and the training includes: using a first section of theconditioning model, configuring at least one hidden layer of the samplemodel in accordance with a first task, wherein the first task includesminimizing a difference between the audio output data and correspondingtraining data; using a second section of the conditioning model,configuring the at least one hidden layer of the sample model inaccordance with a second task, wherein the second task includesmaximizing a metric of perceived quality of the audio output data;including the first section of the conditioning model in the speechmodel, and discarding the second section of the conditioning model; andgenerating, using first text data and the speech model, first audiooutput data corresponding to the input text data.
 2. Thecomputer-implemented method of claim 1, wherein maximizing the metric ofperceived quality comprises at least one of: computing, using the audiooutput data, a perceptual evaluation of speech quality (PESQ) standard;and computing, using the audio output data, a mel-frequency cepstrum(WC).
 3. The computer-implemented method of claim 1, further comprising:selecting a layer of the output model for training the at least onehidden layer of the sample model; generating, during configuring the atleast one hidden layer of the sample model in accordance with the secondtask using the layer of the output model, output feedback data; andtraining the at least one hidden layer of the sample model with theoutput feedback data.
 4. The computer-implemented method of claim 1,further comprising, during the training, removing one or more nodes tocreate the hidden layer, wherein configuring the at least one hiddenlayer of the sample model in accordance with the second task comprisesconfiguring unremoved nodes in the hidden layer.
 5. Acomputer-implemented method comprising: receiving text data; receivingtext metadata corresponding to the text data; and generating, using thetext data, the text metadata, and a speech model, first audio outputdata corresponding to the text data, wherein the speech model includes:a hidden layer having values determined at least in part by: a firsttask using a first section of a model, and a second task using a secondsection of the model, wherein the second task includes maximizing ametric of perceived quality of the first audio output data, the firstsection of the model, and wherein the speech model does not include thesecond section of the model.
 6. The computer-implemented method of claim5, wherein the first section of the model corresponds to a first sectionof a conditioning model, and wherein the second section of the modelcorresponds to a second section of the conditioning model.
 7. Thecomputer-implemented method of claim 5, wherein the first section of themodel corresponds to a first section of an output model, and wherein thesecond section of the model corresponds to a second section of theoutput model.
 8. The computer-implemented method of claim 5, wherein thefirst task comprises measuring an accuracy of the speech model, andfurther comprising comparing the first audio output data tocorresponding audio training data.
 9. The computer-implemented method ofclaim 5, further comprising: training, using a section of a secondmodel, the at least one hidden layer of the speech model in accordancewith the second task.
 10. The computer-implemented method of claim 5,wherein maximizing the metric of perceived quality comprises at leastone of: computing, using the first audio output data, a perceptualevaluation of speech quality (PESQ) standard; and computing, using thefirst audio output data, a mel-frequency cepstrum (MFC).
 11. Thecomputer-implemented method of claim 5, further comprising, duringmaximizing the metric of perceived quality of the first audio outputdata, reducing a number of nodes of the hidden layer.
 12. Thecomputer-implemented method of claim 11, further comprising determininga second speech model using an output of the hidden layer.
 13. A systemcomprising: at least one processor; and at least one memory includinginstructions that, when executed by the at least one processor, causethe system to: receive text data; receive text metadata corresponding tothe text data; and generate, using the text data, the text metadata, anda speech model, first audio output data corresponding to the text data,wherein the speech model includes: a hidden layer having valuesdetermined at least in part by: a first task, using a first section of amodel, and a second task, using a second section of the model, whereinthe second task includes maximizing a metric of perceived quality of thefirst audio output data, the first section of the model, and wherein thespeech model does not include the second section of the model.
 14. Thesystem of claim 13, wherein the first section of the model correspondsto a first section of a conditioning model, and wherein the secondsection of the model corresponds to a second section of the conditioningmodel.
 15. The system of claim 13, wherein the first section of themodel corresponds to a first section of an output model, and wherein thesecond section of the model corresponds to a second section of theoutput model.
 16. The system of claim 13, wherein the first taskcomprises measuring an accuracy of the speech model, and wherein thememory further comprises instructions that, when executed by the atleast one processor, further cause the system to compare the first audiooutput data to corresponding audio training data.
 17. The system ofclaim 13, wherein the memory further comprises instructions that, whenexecuted by the at least one processor, further cause the system totrain, using a section of a second model, the at least one hidden layerof the speech model in accordance with the second task.
 18. The systemof claim 13, wherein the instructions that cause the system to maximizethe metric of perceived quality further cause the system to: compute,using the first audio output data, a perceptual evaluation of speechquality (PESQ) standard; or compute, using the first audio output data,a mel-frequency cepstrum (WC).
 19. The system of claim 13, wherein thememory further comprises instructions that, when executed by the atleast one processor, further cause the system to, during maximizing themetric of perceived quality of the first audio output data, reduce anumber of nodes of the hidden layer.
 20. The system of claim 13, whereinthe memory further comprises instructions that, when executed by the atleast one processor, further cause the system to determine a secondspeech model using an output of the hidden layer.